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        <span>q&amp;a数据补充</span>
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                <a href="/2020/08/08/python%20work/q-a%E6%95%B0%E6%8D%AE%E8%A1%A5%E5%85%85/">q&amp;a数据补充</a>
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                <i class="fa fa-calendar"></i> 2020-08-08
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                    <a href="/2020/08/08/python%20work/q-a%E6%95%B0%E6%8D%AE%E8%A1%A5%E5%85%85/">q&amp;a数据补充</a>
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                <i class="fa fa-calendar"></i> 2020-08-08
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        <h1 id="q-amp-a数据补充"><a href="#q-amp-a数据补充" class="headerlink" title="q&amp;a数据补充"></a>q&amp;a数据补充</h1><p>数据量只有100多条，数据太少，不利于实验的进行</p>
<p>因此考虑采用爬虫的方式，进行数据的补充。</p>
<p>网站： <a target="_blank" rel="noopener" href="http://english.visitbeijing.com.cn/f/list.html?c1=u8e9t0ae">http://english.visitbeijing.com.cn/f/list.html?c1=u8e9t0ae</a> </p>
<p>关于北京旅游的所有信息都有</p>
<p>进行爬虫后，初步筛选数据</p>
<h2 id="读取爬取的旅游文本数据"><a href="#读取爬取的旅游文本数据" class="headerlink" title="读取爬取的旅游文本数据"></a>读取爬取的旅游文本数据</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/5</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">data = pd.read_csv(<span class="string">&#x27;旅游.csv&#x27;</span>)</span><br><span class="line">df = np.array(data).tolist()</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">name = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">    name.append(df[i][<span class="number">0</span>])</span><br><span class="line">names = pd.DataFrame(name, columns=&#123;<span class="string">&#x27;name&#x27;</span>&#125;)</span><br><span class="line">names.fillna(value=<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line">names = np.array(names).tolist()</span><br><span class="line">all_data = []</span><br><span class="line">place = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">    <span class="keyword">if</span> names[i][<span class="number">0</span>] != <span class="number">0</span>:</span><br><span class="line">        all_data.append(df[i])</span><br><span class="line">        place.append(names[i][<span class="number">0</span>])</span><br><span class="line"></span><br><span class="line">price = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    <span class="keyword">if</span> all_data[i][<span class="number">1</span>][<span class="number">0</span>:<span class="number">4</span>] == <span class="string">&#x27;门票价格&#x27;</span>:</span><br><span class="line">        price.append(all_data[i][<span class="number">1</span>][<span class="number">5</span>:].strip())</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        price.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">daoyu = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    daoyu.append(all_data[i][<span class="number">2</span>])</span><br><span class="line"></span><br><span class="line">daoyu = pd.DataFrame(daoyu, columns=&#123;<span class="string">&#x27;daoyu&#x27;</span>&#125;)</span><br><span class="line">daoyu.fillna(value=<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line">daoyu = np.array(daoyu).tolist()</span><br><span class="line"></span><br><span class="line">introduce = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    <span class="keyword">if</span> all_data[i][<span class="number">1</span>][<span class="number">0</span>] == <span class="string">&#x27;导&#x27;</span>:</span><br><span class="line">        introduce.append(all_data[i][<span class="number">1</span>][<span class="number">5</span>:].replace(<span class="string">&#x27; &#x27;</span>, <span class="string">&#x27;&#x27;</span>))</span><br><span class="line"></span><br><span class="line">    <span class="keyword">elif</span> daoyu[i][<span class="number">0</span>] != <span class="number">0</span> <span class="keyword">and</span> daoyu[i][<span class="number">0</span>][<span class="number">0</span>] != <span class="string">&#x27;地&#x27;</span>:</span><br><span class="line">        <span class="keyword">if</span> daoyu[i][<span class="number">0</span>][<span class="number">0</span>] == <span class="string">&#x27;导&#x27;</span>:</span><br><span class="line">            introduce.append(all_data[i][<span class="number">2</span>][<span class="number">5</span>:].replace(<span class="string">&#x27; &#x27;</span>, <span class="string">&#x27;&#x27;</span>))</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        introduce.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">dizhi = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    dizhi.append(all_data[i][<span class="number">3</span>])</span><br><span class="line"></span><br><span class="line">dizhi = pd.DataFrame(dizhi, columns=&#123;<span class="string">&#x27;dizhi&#x27;</span>&#125;)</span><br><span class="line">dizhi.fillna(value=<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line">dizhi = np.array(dizhi).tolist()</span><br><span class="line"></span><br><span class="line">dizhi1 = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    dizhi1.append(all_data[i][<span class="number">4</span>])</span><br><span class="line"></span><br><span class="line">dizhi1 = pd.DataFrame(dizhi1, columns=&#123;<span class="string">&#x27;dizhi&#x27;</span>&#125;)</span><br><span class="line">dizhi1.fillna(value=<span class="number">0</span>, inplace=<span class="literal">True</span>)</span><br><span class="line">dizhi1 = np.array(dizhi1).tolist()</span><br><span class="line"></span><br><span class="line">site = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(all_data)):</span><br><span class="line">    <span class="keyword">if</span> daoyu[i][<span class="number">0</span>] != <span class="number">0</span> <span class="keyword">and</span> daoyu[i][<span class="number">0</span>][<span class="number">0</span>] != <span class="string">&#x27;导&#x27;</span>:</span><br><span class="line">        site.append(daoyu[i][<span class="number">0</span>][<span class="number">5</span>:].replace(<span class="string">&#x27; &#x27;</span>, <span class="string">&#x27;&#x27;</span>))</span><br><span class="line">    <span class="keyword">elif</span> dizhi[i][<span class="number">0</span>] != <span class="number">0</span> <span class="keyword">and</span> dizhi[i][<span class="number">0</span>][<span class="number">0</span>] != <span class="string">&#x27;导&#x27;</span>:</span><br><span class="line">        site.append(dizhi[i][<span class="number">0</span>][<span class="number">5</span>:].replace(<span class="string">&#x27; &#x27;</span>, <span class="string">&#x27;&#x27;</span>))</span><br><span class="line">    <span class="keyword">elif</span> dizhi1[i][<span class="number">0</span>] != <span class="number">0</span> <span class="keyword">and</span> dizhi1[i][<span class="number">0</span>][<span class="number">0</span>] != <span class="string">&#x27;导&#x27;</span>:</span><br><span class="line">        site.append(dizhi1[i][<span class="number">0</span>][<span class="number">5</span>:].replace(<span class="string">&#x27; &#x27;</span>, <span class="string">&#x27;&#x27;</span>))</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        site.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">place = pd.DataFrame(place, columns=&#123;<span class="string">&#x27;place&#x27;</span>&#125;)</span><br><span class="line">price = pd.DataFrame(price, columns=&#123;<span class="string">&#x27;price&#x27;</span>&#125;)</span><br><span class="line">introduce = pd.DataFrame(introduce, columns=&#123;<span class="string">&#x27;introduce&#x27;</span>&#125;)</span><br><span class="line">site = pd.DataFrame(site, columns=&#123;<span class="string">&#x27;site&#x27;</span>&#125;)</span><br><span class="line">datas = pd.concat([place, price, introduce, site], axis=<span class="number">1</span>)</span><br><span class="line">datas.to_excel(<span class="string">&#x27;datas.xlsx&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h1 id="并将新的数据转换为xlsx"><a href="#并将新的数据转换为xlsx" class="headerlink" title="并将新的数据转换为xlsx"></a>并将新的数据转换为xlsx</h1><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line">f = <span class="built_in">open</span>(<span class="string">r&#x27;C:\Users\86184\Desktop\D.txt&#x27;</span>, <span class="string">&#x27;r&#x27;</span>, encoding=<span class="string">&#x27;utf-8&#x27;</span>)</span><br><span class="line"></span><br><span class="line">all_data = []</span><br><span class="line"><span class="keyword">for</span> element <span class="keyword">in</span> f:</span><br><span class="line">    all_data.append(element.split(<span class="string">&quot;\t&quot;</span>))</span><br><span class="line">data = pd.DataFrame(all_data)</span><br><span class="line">data.to_excel(<span class="string">&#x27;new_data.xlsx&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h2 id="生成对应所需的问题"><a href="#生成对应所需的问题" class="headerlink" title="生成对应所需的问题"></a>生成对应所需的问题</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/5</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> random</span><br><span class="line"></span><br><span class="line">data = pd.read_excel(<span class="string">&#x27;new_data.xlsx&#x27;</span>)</span><br><span class="line">df = np.array(data).tolist()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 生成价格的问题</span></span><br><span class="line">price0 = <span class="string">&#x27;How much is the entrance fee of the &#x27;</span></span><br><span class="line">price1 = <span class="string">&#x27;How much is the ticket of the &#x27;</span></span><br><span class="line">price_list = []</span><br><span class="line">price_asnw = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">    <span class="keyword">if</span> df[i][<span class="number">1</span>] == <span class="string">&#x27;0&#x27;</span>:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        <span class="keyword">if</span> <span class="number">0</span> == random.randint(<span class="number">0</span>, <span class="number">1</span>):</span><br><span class="line">            price_list.append(price0 + df[i][<span class="number">0</span>])</span><br><span class="line">            price_asnw.append(df[i][<span class="number">1</span>])</span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            price_list.append(price1 + df[i][<span class="number">0</span>])</span><br><span class="line">            price_asnw.append(df[i][<span class="number">1</span>])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 生成导语</span></span><br><span class="line">tell0 = <span class="string">&#x27;Tell me something about the &#x27;</span></span><br><span class="line">tell1 = <span class="string">&#x27;description of the &#x27;</span></span><br><span class="line"></span><br><span class="line">tell_list = []</span><br><span class="line">tell_asnw = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">    <span class="keyword">if</span> <span class="number">0</span> == random.randint(<span class="number">0</span>, <span class="number">1</span>):</span><br><span class="line">        tell_list.append(tell0 + df[i][<span class="number">0</span>])</span><br><span class="line">        tell_asnw.append(df[i][<span class="number">2</span>])</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        tell_list.append(tell1 + df[i][<span class="number">0</span>])</span><br><span class="line">        tell_asnw.append(df[i][<span class="number">2</span>])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 生成地址</span></span><br><span class="line"></span><br><span class="line">where = <span class="string">&#x27;Where is the &#x27;</span></span><br><span class="line"></span><br><span class="line">site_list = []</span><br><span class="line">site_asnw = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data)):</span><br><span class="line">    site_list.append(where + df[i][<span class="number">0</span>])</span><br><span class="line">    site_asnw.append(df[i][<span class="number">3</span>])</span><br><span class="line"></span><br><span class="line">price = pd.DataFrame(price_list)</span><br><span class="line">tell = pd.DataFrame(tell_list)</span><br><span class="line">site = pd.DataFrame(site_list)</span><br><span class="line">question = pd.concat([price, tell, site], axis=<span class="number">0</span>)</span><br><span class="line">price_a = pd.DataFrame(price_asnw)</span><br><span class="line">tell_a = pd.DataFrame(tell_asnw)</span><br><span class="line">site_a = pd.DataFrame(site_asnw)</span><br><span class="line">answer = pd.concat([price_a, tell_a, site_a], axis=<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">QA = pd.concat([question, answer], axis=<span class="number">1</span>)</span><br><span class="line">QA.to_excel(<span class="string">&#x27;Q&amp;A pairs.xlsx&#x27;</span>)</span><br></pre></td></tr></table></figure>

<h2 id="价格的答案需要更改"><a href="#价格的答案需要更改" class="headerlink" title="价格的答案需要更改"></a>价格的答案需要更改</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/5</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line">data = pd.read_excel(<span class="string">&#x27;Q&amp;A pairs.xlsx&#x27;</span>)</span><br><span class="line">df = np.array(data).tolist()</span><br><span class="line"></span><br><span class="line">answer = <span class="string">&#x27; yuan per person&#x27;</span></span><br><span class="line"></span><br><span class="line">ans = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">165</span>):</span><br><span class="line">    <span class="keyword">if</span> df[i][<span class="number">1</span>] == <span class="string">&#x27;free&#x27;</span>:</span><br><span class="line">        ans.append(df[i][<span class="number">1</span>])</span><br><span class="line">    <span class="keyword">elif</span> df[i][<span class="number">1</span>][<span class="number">5</span>] == <span class="string">&#x27;R&#x27;</span>:</span><br><span class="line">        ans.append(<span class="string">&#x27;About &#x27;</span> + df[i][<span class="number">1</span>][<span class="number">9</span>:] + answer)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        ans.append(<span class="string">&#x27;About &#x27;</span> + df[i][<span class="number">1</span>][<span class="number">5</span>:] + <span class="string">&#x27; per person&#x27;</span>)</span><br><span class="line"></span><br><span class="line">anss = pd.DataFrame(ans)</span><br><span class="line">anss.to_excel(<span class="string">&#x27;ans.xlsx&#x27;</span>)</span><br></pre></td></tr></table></figure>

<h2 id="比较所有的数据和原始数据的重复情况"><a href="#比较所有的数据和原始数据的重复情况" class="headerlink" title="比较所有的数据和原始数据的重复情况"></a>比较所有的数据和原始数据的重复情况</h2><p>并删除重复后的生成数据</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/8/5</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"></span><br><span class="line">data0 = pd.read_excel(<span class="string">&#x27;Q&amp;A pairs.xlsx&#x27;</span>)</span><br><span class="line">data1 = pd.read_csv(<span class="string">&#x27;Q&amp;A pairs1.csv&#x27;</span>)</span><br><span class="line"></span><br><span class="line">df0 = np.array(data0).tolist()</span><br><span class="line">df1 = np.array(data1).tolist()</span><br><span class="line"></span><br><span class="line">print(<span class="built_in">len</span>(df0))</span><br><span class="line"><span class="comment"># 进行查重</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data0)):</span><br><span class="line">    <span class="keyword">for</span> j <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(data1)):</span><br><span class="line">        <span class="keyword">if</span> df0[i][<span class="number">0</span>] == df1[j][<span class="number">0</span>]:</span><br><span class="line">            print(<span class="string">f&#x27;数据0第<span class="subst">&#123;i&#125;</span>个问题与数据1的第<span class="subst">&#123;j&#125;</span>个一样&#x27;</span>)</span><br><span class="line"></span><br><span class="line"><span class="keyword">del</span> df0[<span class="number">1</span>]</span><br><span class="line"><span class="keyword">del</span> df0[<span class="number">396</span>]</span><br><span class="line">print(<span class="built_in">len</span>(df0))</span><br><span class="line"></span><br><span class="line">shuju0 = pd.DataFrame(df0)</span><br><span class="line">shuju1 = pd.DataFrame(df1)</span><br><span class="line">shuju = pd.concat([shuju1, shuju0], axis=<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">shuju.to_excel(<span class="string">&#x27;shuju.xlsx&#x27;</span>)</span><br></pre></td></tr></table></figure>

<p><img src="https://ss2.bdstatic.com/70cFvnSh_Q1YnxGkpoWK1HF6hhy/it/u=234291938,405007536&fm=26&gp=0.jpg"></p>

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